Machines are learning - good for them. What about humans? Popular buzz around now is about machine learning and artificial intelligence. Never in the past, I think these terms intelligence and learning - have become so much importance and got prime time media coverage than now. Thanks, ironically to the qualifiers attached to these words - Artificial and Machine. Now days more engineers are investing time in learning how machines learn (what a paradox) and intelligence that is fake... sorry artificial gets more funding and attention. Has value and quality of human intelligence gone down or has human learning stopped ?
One of the common and popular use case or illustration of machine learning is that now a machine (a software program actually) can recognize picture of a cat or an apple, several types of apples and cats without being explicitly coded do that. Whats more ? As this program "sees"more and more apples and cats - it "learns" - gets better at accuracy at identifying objects. That's quick machine learning intro for you.
When someone takes this idea of identification of car/apple by machine and asks "why cannot machine identify a software bug - as this person does in introduction of this video (at 1:09) - a paradigm shift is needed.
Let us face it - what are in common between a program identifying a cat or an apple on the screen to some other program identifying a bug in a software ?
1. A program with its code and machine learning capability- does its job with relatively simple and formally defined model. There would be rules and patterns in the model to assist the identification. Where as when it comes to form, shape and identification marks for a software bug - you will really struggle to define it.A machine learning model that can recognize a software bug needs far deeper and complicated definition of bug.
2. Even if you concede - you have managed to define a model that can recognize a software bug, the real challenge would be identifying it in a real time when software is running.
Identifying a software bug in simple sense would need following
- Mechanism to generate loads of inputs and configurations of systems under test
- Mechanism to operate SUT with these data sets and observe potentially large number of possible software behaviors
- Among possible outcomes - identify the buggy behavior (Oracle problem)
In short - these are hard problems of software testing in the first place. How machine learning can help?
I like what Paul Merrill says at the end of this talk on youtube talk - "Machines are learning. Are we"(testers) ?
One of the common and popular use case or illustration of machine learning is that now a machine (a software program actually) can recognize picture of a cat or an apple, several types of apples and cats without being explicitly coded do that. Whats more ? As this program "sees"more and more apples and cats - it "learns" - gets better at accuracy at identifying objects. That's quick machine learning intro for you.
When someone takes this idea of identification of car/apple by machine and asks "why cannot machine identify a software bug - as this person does in introduction of this video (at 1:09) - a paradigm shift is needed.
Let us face it - what are in common between a program identifying a cat or an apple on the screen to some other program identifying a bug in a software ?
1. A program with its code and machine learning capability- does its job with relatively simple and formally defined model. There would be rules and patterns in the model to assist the identification. Where as when it comes to form, shape and identification marks for a software bug - you will really struggle to define it.A machine learning model that can recognize a software bug needs far deeper and complicated definition of bug.
2. Even if you concede - you have managed to define a model that can recognize a software bug, the real challenge would be identifying it in a real time when software is running.
Identifying a software bug in simple sense would need following
- Mechanism to generate loads of inputs and configurations of systems under test
- Mechanism to operate SUT with these data sets and observe potentially large number of possible software behaviors
- Among possible outcomes - identify the buggy behavior (Oracle problem)
In short - these are hard problems of software testing in the first place. How machine learning can help?
I like what Paul Merrill says at the end of this talk on youtube talk - "Machines are learning. Are we"(testers) ?
1 comment:
Hi Shrini, you have captured the pointers well. In today's context the learning is not only applicable for machine, to equip up to the speed we humans also need to boot-up ourselves.
While ML needs lot of data & pattern to recognize and learn for long duration to improve the accuracy to sustain in market, for humans typically in testing to cope-up behavior, thinking and attention-to-detail is mandatory for sustain. This keeps up the passion as well nowadays technology is keep on demanding and this is the way to scale.
Post a Comment